Queensland University of Technology · AMB201 Marketing and Audience Research Quantitative Project...

AMB201 MarketingandAudienceResearch QuantitativeProject

MikaelaSpencer StudentNumber:n9456171 DueDate:30/10/2016 1

Queensland University of Technology AMB201 Marketing and Audience Research

Quantitative Project Mikaela Spencer

Student Number: n9456171 Due Date: Sunday, 30th October 2016

Tutor: Jay Kim Word Count: 2200



Participation Reflection I participated in two research studies including Digital Influencers and Instagram Posts

and Brands and Marketing Communications. Both studies required my quantitative

answers to the research questions.

Participation with Digital Influencers and Instagram Posts required my perusal and

analysation of sponsored posts made on a digital influencers Instagram account. I was

asked how well the certain product that was being advertised sat with the vision of the

influencers account and to rate how likely I was to act on that advertisement. I was

also asked how likely I was to contribute to the post via means of “likes”, comments or

further research of the product.

Participation with Brands and Marketing Communications required my perusal and

analysation of a range of brands and their various social media posts. The study was

broken in two five parts. The first required my inspection of the social media posts.

The second required my ability to solve a puzzle to test my spatial awareness. The

third was to choose which brand conveyed the most information in their post. The

fourth was another chance at the puzzle and the fifth stage was to remember what

brand had sponsored which event or not. This study was used to develop a better

understanding of how people evaluate information about upcoming events, involving

brands and marketing activities.

Throughout participating in both studies; I learned the importance of engaging a

respondent in the research. The puzzle provided in the second and fourth stage of

Brands and Marketing Communications allowed me to stay focused and present within

the study. My participation in these studies allowed for a better understanding of the

value of respondents unbiased and objective answers. It also allowed me to better

understand the importance of investigators participating in the research process.



Executive Summary This report contains the analysis of quantitative data relating to attitudes towards

online shopping of male and female Australian consumers aged between eighteen to

forty years of age and over forty years of age. The examination of the data was split

into three methods to directly target the research objectives.

T-tests were implanted to target objective one, to examine if attitudes toward online

retail shopping differs across population segments. The t-tests showed varying results

across the two sub-groups examined. The mean attitude ratings showed that the

younger cohort was slightly higher than the older cohort, but the t-test results showed

significant difference. Whereas, the mean attitude ratings between the genders was

only a fraction of a percentage different and the t-test result showed that there was not

significant difference between the two groups.

Correlation tests were implemented to target objective two; to understand the

relationship between individual characteristics and attitudes toward online retail

shopping. There was found to be significant negative correlation between the attitude

toward online shopping and risk aversion. Whereas, there is significant positive

correlation between the attitude toward online shopping and price consciousness.

Bivariate analysis was implemented to target objective three; to determine which

individual characteristics can be used to predict attitudes toward online retail shopping.

Focused mainly on the adjusted R value and the standardised coefficient of the

attitude towards online shopping and risk aversion and price consciousness,

respectfully, predictions could potentially be made. It was found that there was a small

but significant negative difference on the ability to predict behaviours from this data for

the risk aversion construct. Whereas, there was a small positive relationship on the

ability to predict behaviour using this data for the price consciousness construct.

The recommendations will advocate for a clear structured marketing campaign

targeting the specific constructs and their attitude toward online shopping.

Misrepresented data and representativeness of the subgroups are the two focal

limitations.



Table of Contents

Participation Reflection................................................................................................2Executive Summary......................................................................................................31.0 Introduction and Background..........................................................................6

1.1 Importance of the Research...........................................................................................61.2 Scope of the Report.........................................................................................................61.3 Research Question .......................................................................................................71.4 Aims and Objectives.......................................................................................................7

2.0 Method......................................................................................................................82.1 Methodological Considerations and Assumptions....................................................82.2 Sample Considerations...................................................................................................82.3 Data Collection, Framework and Analytical Considerations....................................9

3.0 Ethical Considerations.........................................................................................104.0 Analysis..................................................................................................................11

4.1 Data Cleaning and Editing............................................................................................114.2 Descriptives....................................................................................................................11

Figure 1.0: Descriptive Statistics............................................................................................11Figure 1.1: Age Cohort.............................................................................................................12Figure 1.2: Gender....................................................................................................................12Figure 1.3 Relationship Status................................................................................................12Figure 1.4 Online Communication..........................................................................................13Figure 1.5 Age and Gender Cross Tabulation......................................................................13Figure 1.6 Relationship Status and Age Cross Tabulation.................................................13Figure 1.7 Online Communication Method and Age Cross Tabulation.............................14Figure 1.8 Distribution of Ages................................................................................................14

4.3 Analysis of Objective One............................................................................................15Figure 2.1: Age Cohort Descriptive Statistics.......................................................................15Figure 2.2: Age Cohort T-Test................................................................................................15Figure 2.3: Gender Descriptive Statistics..............................................................................16Figure 2.4: Gender T-Test.......................................................................................................16

4.4 Analysis of Objective Two............................................................................................17Risk Aversion.............................................................................................................................17Figure 3.1: Correlation between attitude and risk aversion................................................17Price Consciousness................................................................................................................17Figure 3.2: Correlation between attitude and price consciousness...................................17

4.5 Analysis of Objective Three.........................................................................................18Figure 4.1: Risk Adverse Construct.......................................................................................18Figure 4.2: Risk Adverse Model Summary............................................................................18Figure 4.3: Risk Adverse ANOVA...........................................................................................19Figure 4.4: Risk Adverse Coefficients....................................................................................20Figure 4.5: Price Conscious Construct..................................................................................21Figure 4.6: Price Conscious Model Summary......................................................................21Figure 4.7: Price Conscious ANOVA.....................................................................................22Figure 4.8: Price Conscious Coefficients..............................................................................22

5.0 Discussion and Recommendations...................................................................235.1 Objective One.................................................................................................................235.2 Objective Two.................................................................................................................24



5.3 Objective Three..............................................................................................................245.4 Marketing Recommendations......................................................................................25

6.0 Limitations.............................................................................................................267.0 References.............................................................................................................278.0Appendices................................................................................................................28

8.1YoungerAgeSurveyData...................................................................................................288.2OlderAgeSurveyData........................................................................................................31



1.0 Introduction and Background 1.1 Importance of the Research Online sales account for 6.8% of all Australian retail spending which equates roughly

to 20.1 billion dollars’ worth of expenditure, with this figure only expected to rise (NAB

Online Retails Sales Index, 2016). The research into understanding attitudes toward

online retail shopping will help to spread light on what motivates consumers to

purchase products and services online rather than in store.

This report is currently relevant to practitioners as examining consumer attitudes

towards online shopping aids in the marketing process and overall growth of the

industry (Akbar, 2014). Understanding consumer segments and their specific

behaviours made when purchasing online will further develop online retail strategy

(Ganesh, 2010). This report will strengthen existing marketing theories by providing

further evidence and research into the psyche of consumers.

This report will further findings made in the qualitative report by supporting with

quantitative data. The analysis in this document will further explore the research

question and objectives raised. This allows for researchers to have a broad

understanding of the qualitative and quantitative data gathered in exploration of this

topic.

1.2 Scope of the Report This report is undertaken using a quantitative research method and is structured

around three specific research objectives. Generally, this report is focused on the

attitudes toward online shopping held by English-speaking Australian adults who

frequently use the internet. Analysis of the objectives will deepen understanding of

online purchasing behaviours of the population.

The report is not specific to any industry, service or product but rather what can be

accessed across online shopping by the population. This report is using self-recorded

data from surveys and observed behaviour of the participants is not included.

Consumer segments are deconstructed within the analysis.



1.3 Research Question

The research question raised in this report is what are the determinants of Australian

consumers’ attitudes toward online retail shopping. The three dimensions of affective,

cognitive and behavioral attitude will be explored throughout.

1.4 Aims and Objectives The aim of this report is to quantitatively examine determinants of Australian

consumers’ attitudes towards online retail shopping. Investigation of the extent of

differences between subgroups is undertaken through the following specific

objectives:

I) To examine if attitudes toward online retail shopping differs across population

segments;

ii) To understand the relationship between individual characteristics and

attitudes toward online retail shopping;

iii) To determine which individual characteristics can be used to predict

attitudes toward online retail shopping.



2.0 Method 2.1 Methodological Considerations and Assumptions This report employs a quantitative research method to allow for statistical analysis to

determine the extent of differences across subgroups with regards to online retail

shopping. Considerations of this research include the objectivity of the respondent,

the representativeness of all subgroups and the accuracy of the data. It is assumed

that the data was pooled from respondents that fit the requirements of the study.

This report is conducted as a form of descriptive research. Surveys administered to

those of various ages and genders are used to gauge characteristics of the population

but furthermore specific target samples within. Descriptive research is used to

measure consumer behaviours and habits which appropriates it for the method for this

project (Zikmund, 2011).

2.2 Sample Considerations The target population for this report is Australian English-speaking adults the age of

eighteen and over who regularly use the internet. The report is based on the objective

answers by both male and female participants from two age cohorts, eighteen to forty

and forty plus. The sample size is slightly unbalanced with regards to gender with

44.7% females represented and 55.3% males represented. The younger age cohort

was represented by 50.3% and the older generation by 49.7%. The data from each

age group and gender was collected separately allowing for easy analysation.

Non-probability sampling was employed as some elements of the population have

zero probability to being selected. As it was a requirement of the study to reach certain

targets, the selection was not random and there were persons who did not meet these

necessities. The sample frame came from either two females or two males known to

the researcher with one being in the younger age cohort and one in the older. This

sampling approach was used for its convenience and efficiency in collating the data.



2.3 Data Collection, Framework and Analytical Considerations The data for this report was collected in three phases. The first phase required the

participants of either gender and age cohort to respond to the survey disseminated by

the researcher. The second phase was to collect the surveys from the respondents

and upload the results to the database. Finally, the data was cleaned, edited and

analysed using SPSS software. SPSS is technology that performs complex data

handling and analysis (Wire, 2016). The surveys were designed in mind for

quantitative analysis, so questions posed were closed ended in order for a specific

answer to be given, unlike the questions posed for the qualitative analysis that were

open ended where subjectivity could occur. Respondents were asked to rate on a

scale of one to seven how much they agree with a statement.

The survey framework was specifically focused on targeting the three research

objectives of; to examine if attitudes toward online retail shopping differ across

population segments; to understand the relationship between individual characteristics

and attitudes toward online retail shopping; and, to determine which individual

characteristics can be used to predict attitudes toward online retail shopping. The three

objectives contributed to the focal construct of the paper which was the study of the

attitudes towards online retail shopping, which can be considered as having three

dimensions including; attitude (affective); attitude (cognitive); and attitude

(behavioural).


MikaelaSpencer StudentNumber:n9456171 DueDate:30/10/201610

3.0 Ethical Considerations The research conducted for the formulation of this report was in line with current

Queensland University of Technology (QUT, 2016) and Australian Market and Social

Research Society (AMSRS, 2016) ethical code. The latter entity covers standards of

practice for respondent’s rights, researcher’s professional responsibilities and

researchers and client’s mutual rights and responsibilities (AMSRS, 2016). The study

was sanctioned under QUT Ethics Approval Number 1500000542. Participants of the

study were provided with the relevant information about what was being investigated,

the expected benefits of the study, the risks involved, privacy and confidentiality

matters, and contact information of the unit co-ordinator. Following being provided with

this information, participants were asked to read and, if in agreement, sign a consent

form so that the study could be undertaken. Under QUT Code of Conduct, the consent

forms and the surveys are kept securely in storage. The surveys themselves do not

contain any identifiable markers to maintain the privacy of the respondent.

To publish ethical research, the conduction of the survey was administered fairly with

the same standardised questions being asked to all members of the population. The

research must be conducted in an ethical fashion as the report will have an impact on

the online retail shopping and if prejudiced research was conducted, this could

negatively effect the report and thus, the industry (Wester, 2011). Research is

underpinned by the participation of individuals to provide their unbiased, objective

opinion. Ethics in research conduction provides the participant with the security that

their opinions will be used discretely and their privacy maintained. If researchers do

not conduct projects in an ethical fashion, this will cause these vital individuals to

rethink about contributing in future studies (Zikmund, 2011).



4.0 Analysis 4.1 Data Cleaning and Editing The data collated for examination was cleaned and edited so that deeper

understanding of the results could be undertaken. Data cleaning and editing involves

the systematic analysis of the results to remove any inaccurate or incomplete data

entries. The reason as to why data is subject to this process is because incorrect

statistical analysis can negatively affect the objectivity of the results. The cleaning of

this data was completed using SPSS software which removed uninterpretable

responses, non-existent postcodes and also estimated approximations. Negatively

phrased survey items were subject to reverse coding and construct computations were

determined for each respondent by averaging across the pertinent items.

4.2 Descriptives The final size of the sample after cleaning and editing was 702 participants. Figure 1.0

shows the overall mean and standard deviation for each construct relevant to the

analysis, as well as the minimum and maximum scores given by respondents in the

dataset.

Figure 1.0: Descriptive Statistics

The highest recorded mean is for Price Consciousness at 4.9765 and the lowest at

3.6955 for Impulsiveness.



Figure 1.1 depicts the split of the age group in the population between eighteen to

forty and forty and over.

Figure 1.1: Age Cohort

Figure 1.2 depicts the split of the gender group between male and female participants.

Figure 1.2: Gender

Figure 1.3 depicts the split between single and partnered respondents

Figure 1.3 Relationship Status



Figure 1.4 depicts the method of online communication frequently used by participants

Figure 1.4 Online Communication

Figure 1.5 depicts the cross tabulation between age and gender in the cohort. Cross

tabulations show the relationship the two variables share. Out of 388 males, 192 were

forty and under and 196 were over the age of forty. Out of 314 females, 161 were forty

and under and 153 were over the age of forty.

Figure 1.5 Age and Gender Cross Tabulation

Figure 1.6 depicts the cross tabulation between age cohort and relationship status.

Figure 1.6 Relationship Status and Age Cross Tabulation



Figure 1.7 depicts the cross tabulation between online communication method and

age.

Figure 1.7 Online Communication Method and Age Cross Tabulation

Figure 1.8 displays the spread of respondents’ ages. The most common age in the

under forty plus cohort is twenty and there are no respondents who are thirty-six years

of age. The forty plus cohort has a more even distribution with fifty-four and fifty-six

the most common age brackets within the demographic.

Figure 1.8 Distribution of Ages



4.3 Analysis of Objective One The purpose of objective one is to examine if attitudes toward online retail shopping

differs across population segments. To address this objective, t-tests can be

conducted. A t-test is used to compare two groups to answer questions such as does

attitude toward online retail shopping (ATTBI) differ between younger and older

people? SPSS output appears below.

Does attitude toward online retail shopping (ATTBI) differ between younger and older

people?

Figure 2.1: Age Cohort Descriptive Statistics In this first table (above) the descriptive statistics for the two groups are shown. It can

be seen that the mean attitude rating for the younger group looks a little higher than

the mean attitude rating for the older group.

Figure 2.2: Age Cohort T-Test

The table above represents the t-test results of the age cohorts. Assuming equal

variances, the t-test result it 9.334 and the Sig. (2-tailed) value is less than 0.05, so it

can be concluded that there is statistically significant difference between the two

groups. Meaning that the mean attitude rating of the younger cohort is significantly

higher than that of the older age group.



Does attitude toward online retail shopping differ between males and females?

Figure 2.3: Gender Descriptive Statistics Descriptive statistics of the two genders examined are shown. It can be seen that the

mean attitude rating for males and females differs by less than one of a percentage.

The female mean is 5.0127 and the male mean is 4.9201, a difference of 0.0926.

Despite this miniscule difference it is still important to examine whether the means are

statistically different. Figure 2.4 displays the t-test for the different gender groups.

Figure 2.4: Gender T-Test Assuming equal variances, the t-value is -780 and the sig value is 0.436. As the sig

value is more than 0.05, it can be assumed that the two means are not significantly

different.



4.4 Analysis of Objective Two The aim of objective two is to understand the relationship between individual

characteristics and attitudes toward online retail shopping. The following analysis

examines the correlation between two variables.

Risk Aversion

Figure 3.1: Correlation between attitude and risk aversion Correlation between attitude toward online shopping and risk aversion is measured at

the Pearson Correlation level of -0.454** and a Sig. (2-tailed) level of 0.000. The sig

value represents that the correlation is significant and the Pearson Correlation level

indicates that the correlation is negative. This means that risk adverse people are more

likely to have a negative relationship with online shopping.

Price Consciousness

Figure 3.2: Correlation between attitude and price consciousness Correlation between the attitude toward online shopping and price consciousness is

measured at the Pearson Correlation level of 0.029 and a Sig. (2-tailed) level of 0.445.

The sig value represents that the correlation is significant and the Pearson Correlation

level indicates that the correlation is positive. This means that price conscious people

are more likely to have a positive relationship with online shopping.



4.5 Analysis of Objective Three The purpose of objective three is to determine which individual characteristics can be

used to predict attitudes toward online retail shopping. The following analysis builds

on the concept of correlation by using bivariate regression to serve as an indicator of

the significance of the relationship between two variables. This also allows predictions

to be made about one variable using the other. Bivariate regression produces a linear

equation calculated to a set of observed data which will then be used to make

predictions about the dependent variable when the value of the independent variable

is known.

Risk Aversion

The relationship between the attitude towards online shopping and risk adverse and

price conscious respondents was analysed.

Figure 4.1: Risk Adverse Construct The first table (above) lists the constructs entered into the regression analysis. Here,

RA (risk aversion) is described as a predictor or independent variable, and ATTBI

(attitudes toward online shopping) is described as the dependent variable.

Figure 4.2: Risk Adverse Model Summary



The Model Summary above displays the results of R, R Squared and adjusted R

Squared between the risk adverse variable and the attitude variable. The R value is

indicating an average relationship for the variables as it is only 0.454. The R Squared

value is 0.206 representing a small proportion of variance in the dependent variable

indicating that it may be of little predictive value. Adjusted R Squared is reported at

0.205 indicating that 20.5% of variation in attitudes (RA) is explained in this model.

The analysis of variance table displays a significance level of 0.00. This indicates the

equation explores the variation in the dependent variable well.

Figure 4.3: Risk Adverse ANOVA The ANOVA results showed the model to be useful so analysis of the coefficients was

then undertaken. The significance of the RA predictor is 0.00, showing that the

construct has a specific impact on the ATTBI.



Figure 4.4: Risk Adverse Coefficients Standardised coefficients can determine the nature of the relationship between the

predictor and the dependent variable. The standardised coefficient between RA and

ATTBI is -0.454 displaying that the predictor has a small but significant negative effect

on ATTBI. The predicted ATTBI score 8.441 will decrease by 0.746 units for each one-

unit increase in a person’s Risk Aversion score.

Predictions can be made about respondent’s attitudes towards online shopping. This

is done using a regression equation calculated using the unstandardised coefficients

(B) column. To calculate ATTBI prediction the following equation can be used:

ATTBI prediction = 8.441 -0.746*(Risk Aversion score)

This equation is not unflawed, however will give a good indicator of predictions about

a risk adverse respondents potential attitude towards online shopping.



Price Consciousness

Figure 4.5: Price Conscious Construct The first table (above) lists the constructs entered into the regression analysis. Here,

PC (Price Consciousness) is described as a predictor or independent variable, and

ATTBI (attitudes toward online shopping) is described as the dependent variable.

Figure 4.6: Price Conscious Model Summary The Model Summary above displays the results of R, R Squared and adjusted R

Squared between the risk adverse variable and the attitude variable. The R value is

indicating a weak relationship for the variables as it is only 0.029. The R Squared value

is 0.001 representing a small proportion of variance in the dependent variable

indicating that it may be of little predictive value. Adjusted R Squared is reported at

-0.001 indicating that -0.001% of variation in attitudes (PC) is explained in this model.



The analysis of variance table displays a significance level of -0.445. This indicates

the equation explores the variation in the dependent variable well.

Figure 4.7: Price Conscious ANOVA The ANOVA results showed the model to be useful so analysis of the coefficients was

then undertaken. The significance of the RA predictor is -0.445, showing that the

construct has a specific impact on the ATTBI.

Figure 4.8: Price Conscious Coefficients Standardised coefficients can determine the nature of the relationship between the

predictor and the dependent variable. The standardised coefficient between PC and

ATTBI is -0.029 displaying that the predictor has a small but significant positve effect

on ATTBI. The predicted ATTBI score 4.731 will decrease by 0.046 units for each one-

unit increase in a person’s Price Consciousness score.

Predictions can be made about respondent’s attitudes towards online shopping. This

is done using a regression equation calculated using the unstandardised coefficients

(B) column. To calculate ATTBI prediction the following equation can be used:

ATTBI prediction = 4.731 -0.046*(Price Consciousness score)

This equation is not unflawed, however will give a good indicator of predictions about

a price consciousness respondents potential attitude towards online shopping.



5.0 Discussion and Recommendations This study contributes to the field of consumer behaviour. From an academic

perspective the paper has analysed determinants of Australian consumers’ attitudes

toward online retail shopping. Each objective was targeted in the analysis and further

discussion for each provided.

5.1 Objective One T-tests were implemented to target objective one; to examine if attitudes toward online

retail shopping differs across population segments. Analysis of these results showed

that although there was only a slight difference in the mean attitude ratings between

the two age cohorts, there was actually significant difference according to the t-tests.

This attitude rating was significantly higher in the younger cohort. The findings were

commonly accepted as it was thought the older generation wouldn’t find online

shopping as favourable as the younger generation.

These correlations provide an insight for marketing managers to examine the attitude

difference across population segments. A recommendation for marketing managers

include further research into the correlation between the two variables and to what

drives the older generation to not feel as strongly favourable to online shopping. After

this research is conducted marketing campaigns can be formulated to target what

troubles older respondents from participating as freely in online shopping. The

correlation provides evidence as to attitude towards online shopping and difference

across population segments.



5.2 Objective Two Correlation theory was used to examine objective two; to understand the relationship

between individual characteristics and attitudes toward online retail shopping. The

characteristics examined for correlation were risk aversion and price consciousness.

There was found to be significant negative correlation between risk aversion and the

attitude online shopping. Whereas, there was found to be significant positive

correlation between price consciousness and the attitude toward online shopping. The

findings were as to be expected.

A marketing manager could use the following results to specifically target and market

campaigns for online shopping to move risk adverse and price conscious consumers.

As attitudes and these specific characteristics cover all age cohorts and genders,

further research into additional characteristics could be made as to complement.

Marketers could specifically campaign online and print advertisement tailored to

consumers with these specific characteristic traits.

5.3 Objective Three Bivariate analysis was implemented to target objective three; to determine which

individual characteristics can be used to predict attitudes toward online retail shopping.

Risk aversion was found to have an adjusted R value of 0.205 and a standardised

coefficient -0.454 meaning that there was a small but significant negative difference

on the ability to predict behaviours from this data. Price consciousness was found to

have an adjusted R value of -0.001 and a standardised coefficient of 0.029 meaning

that there was a small positive relationship on the ability to predict behaviour using

this data. Findings were informative as there was no real pre-conceived ideas.

Marketing managers could use the following information to predict behaviour towards

online shopping attitudes. Marketing managers could implement predictive research

into campaigns to further target specific audiences. Future research could include

further characteristics to be examined and behaviour predicted. Attitude is a key factor

to predicting behavioural intentions and this research is critical to marketers wanting

to predict attitudes towards online shopping and certain characteristics.



5.4 Marketing Recommendations The research has shown that the correlation between characteristics and attitudes

towards online shopping, the relationship between the characteristics and attitudes

and the predictions of behaviours between characteristics and attitudes has pointed

to one clear marketing strategy. That is to clearly target online shopping audiences

with campaigns that directly focus on their behaviour, characteristics and attitudes.

Risk aversion campaigns could include the marketing of secure online payment and

secure storage of personal information by e-Commerce stores. Price conscious

campaigns could include the promotion of cheaper trade online, inexpensive means

of delivery and flash online sales.



6.0 Limitations Quantitative research is subject to certain disadvantages. Data collection is extremely

time consuming and before the data can be analysed it must be entered, cleaned and

edited making data collection a very lengthy process. Analysation of data takes

excessive time to fully understand. This may lead to outdated data being used within

the industry and deadlines being missed.

There are some limitations to the quantitative research undertaken in this study. Issues

with the methodology include the unevenness of the representation of the subgroups,

the objectivity of the respondents and the accuracy of the data. Although all measures

were put in place to avoid these issues, the data was still not wholly untainted. Issues

with the sample include the fact that the two respondents were randomly chosen by

the researchers with the respondents only having to meet a few certain requirements.

There were slightly more males in the sample then females and the older cohort was

slightly underrepresented compared to the younger.

The limitations of the analysation of the data include that analysation of variance for

the t-tests is not examined and multiple regression for the bivariate analysis is not

observed. Family and friends being surveyed by the researchers also presents issues

for the analysis process as the data is not coming from a wide pool.

Misrepresented data presents major issue to quantitative research as the data is what

the analysis, discussion and recommendations are based. If the data is not unbiased,

wrong information can be given and this can lead to issues within the industry after

implementation of recommendations made. The findings of this report can be used

as a point of reference for further research to be built upon. The results were gathered

by university students who maintained the professionalism to the best of their ability,

they are not specialists in their field and unintended error may have been made in the

collation of the data. Future research could include using more specific respondents

and using a more representative pool group.



7.0 References Akbar, S. James, T.J. (2014) Consumers’ attitude towards online shopping: Factors

influencing employees of crazy domains to shop online. Journal of Management &

Marketing Research, 14 (1) 103-116RCE TYPE

Australian Market and Social Research Society (2016). Code of Professional

Behaviour. Retrieved from

http://www.amsrs.com.au/documents/item/194

Bhagat, S (2015) Factors influencing purchase and non-purchase behaviour in online

shopping. Journal of Anvesha, 8 (1), 34-43

Ganesh, J. Reynolds, K. Luckett, M. Pomirleanu, N. (2010) Online Shopper

Motivations, and e-Store Attributes: An Examination of Online Patronage Behavior

and Shopper Typologies. Journal of Retailing, 86 (1) 106–115

National Australia Bank (2016) NAB Online Retail Sales Index: In-depth June 2016.

Retrieved from

http://business.nab.com.au/nab-online-retail-sales-index-june-2016-17897/

Wester, K (2011). Publishing Ethical Research: A Step-by-Step Overview. Journal of

Counselling and Development, 89 (3) 301-307

Zikmund, W. Ward, S, Lowe, B. Winzar, H & Babin, B. (2011). Marketing Research

(2ed.). Nelson Australia Pty Ltd.

Queensland University of Technology (2016) Code of Conduct for Research.

Retrieved from

http://www.mopp.qut.edu.au/D/D_02_06.jsp



8.0Appendices8.1YoungerAgeSurveyData



8.2OlderAgeSurveyData

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